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DBMS > Fujitsu Enterprise Postgres vs. Hazelcast vs. JanusGraph vs. Microsoft Azure Data Explorer vs. Percona Server for MongoDB

System Properties Comparison Fujitsu Enterprise Postgres vs. Hazelcast vs. JanusGraph vs. Microsoft Azure Data Explorer vs. Percona Server for MongoDB

Editorial information provided by DB-Engines
NameFujitsu Enterprise Postgres  Xexclude from comparisonHazelcast  Xexclude from comparisonJanusGraph infosuccessor of Titan  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonPercona Server for MongoDB  Xexclude from comparison
DescriptionEnterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.A widely adopted in-memory data gridA Graph DBMS optimized for distributed clusters infoIt was forked from the latest code base of Titan in January 2017Fully managed big data interactive analytics platformA drop-in replacement for MongoDB Community Edition with enterprise-grade features.
Primary database modelRelational DBMSKey-value storeGraph DBMSRelational DBMS infocolumn orientedDocument store
Secondary database modelsDocument store
Spatial DBMS
Document store infoJSON support with IMDG 3.12Document store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.37
Rank#278  Overall
#128  Relational DBMS
Score5.46
Rank#61  Overall
#7  Key-value stores
Score2.02
Rank#125  Overall
#12  Graph DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.60
Rank#246  Overall
#39  Document stores
Websitewww.postgresql.fastware.comhazelcast.comjanusgraph.orgazure.microsoft.com/­services/­data-explorerwww.percona.com/­mongodb/­software/­percona-server-for-mongodb
Technical documentationwww.postgresql.fastware.com/­product-manualshazelcast.org/­imdg/­docsdocs.janusgraph.orgdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.percona.com/­percona-distribution-for-mongodb
DeveloperPostgreSQL Global Development Group, Fujitsu Australia Software TechnologyHazelcastLinux Foundation; originally developed as Titan by AureliusMicrosoftPercona
Initial release2008201720192015
Current releaseFujitsu Enterprise Postgres 14, January 20225.3.6, November 20230.6.3, February 2023cloud service with continuous releases3.4.10-2.10, November 2017
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache 2.0commercialOpen Source infoGPL Version 2
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCJavaJavaC++
Server operating systemsLinux
Windows
All OS with a Java VMLinux
OS X
Unix
Windows
hostedLinux
Data schemeyesschema-freeyesFixed schema with schema-less datatypes (dynamic)schema-free
Typing infopredefined data types such as float or dateyesyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.yes infothe object must implement a serialization strategynoyesno
Secondary indexesyesyesyesall fields are automatically indexedyes
SQL infoSupport of SQLyesSQL-like query languagenoKusto Query Language (KQL), SQL subsetno
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JCache
JPA
Memcached protocol
RESTful HTTP API
Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
proprietary protocol using JSON
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
Clojure
Java
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk
Server-side scripts infoStored proceduresuser defined functionsyes infoEvent Listeners, Executor ServicesyesYes, possible languages: KQL, Python, RJavaScript
Triggersyesyes infoEventsyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodespartitioning by range, list and by hashShardingyes infodepending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)Sharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyes infoReplicated Mapyesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes infovia Faunus, a graph analytics engineSpark connector (open source): github.com/­Azure/­azure-kusto-sparkyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Immediate Consistency
Eventual Consistency
Immediate Consistency
Eventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyesnoyes infoRelationships in graphsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDone or two-phase-commit; repeatable reads; read commitedACIDnono
Concurrency infoSupport for concurrent manipulation of datayes, multi-version concurrency control (MVCC)yesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoSupports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcastyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes infovia In-Memory Engine
User concepts infoAccess controlfine grained access rights according to SQL-standardRole-based access controlUser authentification and security via Rexster Graph ServerAzure Active Directory AuthenticationAccess rights for users and roles

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More resources
Fujitsu Enterprise PostgresHazelcastJanusGraph infosuccessor of TitanMicrosoft Azure Data ExplorerPercona Server for MongoDB
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